Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing

doi:10.1088/1674-4527/ad339e

	Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing
	Li H(李徽)2,3; Li RW(李荣旺)1,3; Shu P(舒鹏)3; Li YQ(李语强)1,3
刊名	RESEARCH IN ASTRONOMY AND ASTROPHYSICS
	2024-04-01
卷号	24 期号:4
关键词	techniques: image processing methods: data analysis light pollution
ISSN号	1674-4527
DOI	10.1088/1674-4527/ad339e
产权排序	第1完成单位
文献子类	Article
英文摘要	Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal. Analyzing light curves to determine attitude is the most commonly used method. In photometric observations, outliers may exist in the obtained light curves due to various reasons. Therefore, preprocessing is required to remove these outliers to obtain high quality light curves. Through statistical analysis, the reasons leading to outliers can be categorized into two main types: first, the brightness of the object significantly increases due to the passage of a star nearby, referred to as stellar contamination, and second, the brightness markedly decreases due to cloudy cover, referred to as cloudy contamination. The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive. However, we propose the utilization of machine learning methods as a substitute. Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination, achieving F1 scores of 1.00 and 0.98 on a test set, respectively. We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine, then conduct comparative analyses of the results.
学科主题	天文学 ; 天文学其他学科 ; 计算机科学技术 ; 人工智能
URL标识	查看原文
出版地	20A DATUN RD, CHAOYANG, BEIJING, 100101, PEOPLES R CHINA
资助项目	National Natural Science Foundation of China (NSFC)[12373086]; National Natural Science Foundation of China (NSFC)[12303082]; CAS Light of West China Program, Yunnan Revitalization Talent Support Program in Yunnan Province, National Key R&D Program of China[2022YFC2203800]
WOS研究方向	Astronomy & Astrophysics
语种	英语
出版者	NATL ASTRONOMICAL OBSERVATORIES, CHIN ACAD SCIENCES
WOS记录号	WOS:001207482400001
资助机构	National Natural Science Foundation of China (NSFC)[12373086, 12303082] ; CAS Light of West China Program, Yunnan Revitalization Talent Support Program in Yunnan Province, National Key R&D Program of China[2022YFC2203800]
内容类型	期刊论文
版本	出版稿
源URL	[http://ir.ynao.ac.cn/handle/114a53/27126]
专题	云南天文台_应用天文研究组
作者单位	1.Key Laboratory of Space Object and Debris Observation, Chinese Academy of Sciences, Nanjing 210023, China 2.University of Chinese Academy of Sciences, Beijing 100049, China; 3.Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, China; lirw@ynao.ac.cn;
推荐引用方式 GB/T 7714	Li H,Li RW,Shu P,et al. Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2024,24(4).
APA	李徽,李荣旺,舒鹏,&李语强.(2024).Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,24(4).
MLA	李徽,et al."Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 24.4(2024).